In-motion initialization of accelerometer for accurate vehicle positioning

ABSTRACT

Techniques provided herein are directed toward addressing these and other issues by providing robust means for initializing an accelerometer that can take place even while a vehicle is in motion. Specifically, linear acceleration and velocity data can be estimated from wheel speeds, and angular velocity can be estimated with a gyroscope. The vehicle&#39;s acceleration can then be computed from these estimates, and subtracted from a total acceleration measured by the accelerometer to determine gravitational acceleration, which can then be accounted for in subsequent measurements taken by the accelerometer. A vehicle velocity may also be determined based on the vehicle&#39;s estimated angular velocity and linear velocity. Embodiments may also employ techniques for translating measurements taken in one coordinate frame to another coordinate frame for estimate determination and/or outlier compensation.

BACKGROUND

Vehicle systems, such as autonomous driving and advanced driver-assistsystems (ADAS), often need highly-accurate positioning information tooperate correctly. Global Navigation Satellite Systems (GNSS), such asGlobal positioning system (GPS) and/or similar satellite-basedpositioning technologies can provide such positioning data in open skyscenarios. However, the performance of GNSS drastically degrades iflarge parts of the sky are obstructed. This occurs, for example, inso-called “urban canyon” scenarios, where GNSS-based estimated positionsmay be off by as much as 50 m. These large positioning errors can beprohibitive in vehicular automation and/or navigation.

To address these and other issues, position determination may further bebased on data from non-GNSS sources, such a camera, inertial sensor(e.g., gyroscope and/or accelerometer), and/or other sensor indicativeof vehicle motion. Visual-inertial odometry (VIO), for instance,combines camera and inertial sensor data to provide vehicle positioninformation that may be used as a complement and/or substitute ofGNSS-determined vehicle position. Problematically, initialization(initial calibration) of the accelerometer must traditionally be donewhen the vehicle is not in motion, to ensure direct and accuratemeasurement of gravity by the accelerometer. This restriction toinitialization while being stationary, however, can be quite limiting inpractical circumstances.

BRIEF SUMMARY

Techniques provided herein are directed toward addressing these andother issues by providing robust means for initializing an accelerometerthat can take place even while a vehicle is in motion. Specifically,linear acceleration and velocity data can be estimated from wheelspeeds, and angular velocity can be estimated with a gyroscope. Thevehicle's acceleration can then be computed from these estimates, andsubtracted from a total acceleration (often termed the “specific force”)measured by the accelerometer to determine gravitational acceleration,which can then be accounted for in subsequent measurements taken by theaccelerometer. A vehicle velocity may also be determined based on thevehicle's estimated angular velocity and linear velocity. Embodimentsmay also employ techniques for translating measurements taken in onecoordinate frame to another coordinate frame for estimate determinationand/or outlier compensation.

An example method of initializing an accelerometer located on a vehicle,according to the description, comprises estimating a total accelerationbased on measurements from the accelerometer taken during a period oftime, estimating a linear velocity and an acceleration based on wheelrotation measurements of the vehicle taken during the period of time,estimating an angular velocity based on measurements from a gyroscopelocated on the vehicle and taken during the period of time, determiningvehicle acceleration based on the estimated linear velocity, estimatedtotal acceleration, and estimated angular velocity, determininggravitational acceleration based on the estimated total acceleration anddetermined vehicle acceleration, and offsetting, with a processing unitlocated on the vehicle, a subsequent measurement of the accelerometerbased on the determined gravitational acceleration.

Alternative embodiments of the method may comprise one or more of thefollowing features. The method may further comprise determining avehicle velocity using the estimated angular velocity and estimatedlinear velocity, and using the determined vehicle velocity to initializea navigation filter. Estimating the linear velocity and estimating thetotal acceleration may further comprise translating the wheel rotationmeasurements from a first coordinate frame to a second coordinate frame.The accelerometer and the gyroscope may compose at least a portion of aninertial measurement unit (IMU) located on the vehicle. The IMU maycompose a portion of a visual inertial odometry (VIO) system located onthe vehicle. The method may further comprise estimating, with theprocessing unit, a location of the vehicle based at least in part on theoffset subsequent measurement of the accelerometer. The method mayfurther comprise performing outlier compensation on the wheel rotationmeasurements, measurements from the gyroscope, or measurements from theaccelerometer, or any combination thereof. The method may furthercomprise performing outlier compensation on the wheel rotationmeasurements, wherein performing the outlier compensation comprisesdetermining values of staggered differences of the wheel rotationmeasurements. Determining the vehicle acceleration may comprise taking amedian of the determined values of staggered differences. The method mayfurther comprise determining a vehicle velocity at least in part byusing the estimated angular velocity and estimated linear velocity anddetermining a constant for each wheel rotation measurement, based atleast in part on the determined vehicle acceleration. Determining thevehicle velocity further may be based on a median of the constants forthe wheel rotation measurements.

An example mobile computing system, according to the description,comprises a memory and a processing unit. The processing unit iscommunicatively coupled with the memory and configured to estimate atotal acceleration based on measurements received from an accelerometerlocated on a vehicle and taken during a period of time, estimate alinear velocity and an acceleration based on wheel rotation measurementsof the vehicle taken during the period of time, estimate an angularvelocity based on measurements from a gyroscope located on the vehicleand taken during the period of time, determine vehicle accelerationbased on the estimated linear velocity, estimated total acceleration,and estimated angular velocity, determine gravitational accelerationbased on the estimated total acceleration and determined vehicleacceleration, and offset a subsequent measurement of the accelerometerbased on the determined gravitational acceleration.

Alternative embodiments of the mobile computing system may include oneor more the following features. The processing unit may be furtherconfigured to determine a vehicle velocity using the estimated angularvelocity and estimated linear velocity, and use the determined vehiclevelocity to initialize a navigation filter. The processing unit may beconfigured to estimate the linear velocity and configured to estimatethe total acceleration at least in part by translating the wheelrotation measurements from a first coordinate frame to a secondcoordinate frame. The mobile computing system may further comprise anIMU comprising the accelerometer and the gyroscope. The mobile computingsystem may further comprise a visual inertial odometry (VIO) system,which comprises the IMU. The processing unit may be configured toestimate a location of the vehicle based at least in part on the offsetsubsequent measurement of the accelerometer. The processing unit may beconfigured to perform outlier compensation on the wheel rotationmeasurements, measurements from the gyroscope, or measurements from theaccelerometer, or any combination thereof. The processing unit may beconfigured to perform the outlier compensation at least in part bydetermining values of staggered differences of the wheel rotationmeasurements. The processing unit may be configured to determine thevehicle acceleration at least in part by taking a median of thedetermined values of staggered differences. The processing unit may beconfigured to determine a vehicle velocity at least in part by using theestimated angular velocity and estimated linear velocity and determininga constant for each wheel rotation measurement, based at least in parton the determined vehicle acceleration. The processing unit may beconfigured to determine the vehicle velocity further based on a medianof the constants for the wheel rotation measurements.

An example device, according to the description, comprises means forestimating a total acceleration based on acceleration measurements of avehicle taken during a period of time, means for estimating a linearvelocity and an acceleration based on wheel rotation measurements of thevehicle taken during the period of time, means for estimating an angularvelocity based on measurements of rotational movement of the vehicletaken during the period of time, means for determining vehicleacceleration based on the estimated linear velocity, estimated totalacceleration, and estimated angular velocity, means for determininggravitational acceleration based on the estimated total acceleration anddetermined vehicle acceleration, and means for offsetting a subsequentacceleration measurement based on the determined gravitationalacceleration.

Alternative embodiments of the device may further comprise one or moreof the following features. The device may comprise means for determininga vehicle velocity using the estimated angular velocity and estimatedlinear velocity, and means for using the determined vehicle velocity toinitialize a navigation filter. The means for estimating the linearvelocity and the means for estimating the total acceleration may furthercomprise means for translating the wheel rotation measurements from afirst coordinate frame to a second coordinate frame. The device mayfurther comprise means for obtaining the acceleration measurements ofthe vehicle and measurements of rotational movement of the vehicle froman inertial measurement unit (IMU) located on the vehicle. The IMU maycompose a portion of a visual inertial odometry (VIO) means located onthe vehicle. The device may further comprise means for estimating alocation of the vehicle based at least in part on the offset subsequentacceleration measurement. The device may further comprise means forperforming outlier compensation on the wheel rotation measurements,measurements of rotational movement of the vehicle, or accelerationmeasurements of the vehicle, or any combination thereof. The means forperforming outlier compensation on the wheel rotation measurements mayinclude means for determining values of staggered differences of thewheel rotation measurements. The means for determining the vehicleacceleration may comprise means for taking a median of the determinedvalues of staggered differences. The device may further comprise meansfor determining a vehicle velocity at least in part by using theestimated angular velocity and estimated linear velocity and means fordetermining a constant for each wheel rotation measurement, based atleast in part on the determined vehicle acceleration. The means fordetermining the vehicle velocity further is configured to base thedetermining on a median of the constants for the wheel rotationmeasurements.

An example non-transitory computer-readable medium, according to thedescription, has instructions embedded thereon for initializing anaccelerometer located on a vehicle. The instructions when executed byone or more processing units, cause the one or more processing units toestimate a total acceleration based on measurements from theaccelerometer taken during a period of time, estimate a linear velocityand an acceleration based on wheel rotation measurements of the vehicletaken during the period of time, estimate an angular velocity based onmeasurements from a gyroscope located on the vehicle and taken duringthe period of time, determine vehicle acceleration based on theestimated linear velocity, estimated total acceleration, and estimatedangular velocity, determine gravitational acceleration based on theestimated total acceleration and determined vehicle acceleration, andoffset a subsequent measurement of the accelerometer based on thedetermined gravitational acceleration.

Alternative embodiments of the non-transitory computer-readable mediummay comprise one or more of the following features. The non-transitorycomputer-readable medium may further comprise instructions that, whenexecuted by the one or more processing units, further cause the one ormore processing units to determine a vehicle velocity using theestimated angular velocity and estimated linear velocity, and use thedetermined vehicle velocity to initialize a navigation filter. Theinstructions for estimating the linear velocity and the instructions forestimating the total acceleration may further comprise instructionsthat, when executed by the one or more processing units, further causethe one or more processing units to translate the wheel rotationmeasurements from a first coordinate frame to a second coordinate frame.The non-transitory computer-readable medium may further compriseinstructions that, when executed by the one or more processing units,further cause the one or more processing units to obtain themeasurements from the accelerometer and the measurements from thegyroscope from an inertial measurement unit (IMU) located on thevehicle. The non-transitory computer-readable medium may furthercomprise instructions that, when executed by the one or more processingunits, further cause the one or more processing units to estimate, alocation of the vehicle based at least in part on the offset subsequentmeasurement of the accelerometer. The non-transitory computer-readablemedium may further comprise instructions that, when executed by the oneor more processing units, further cause the one or more processing unitsto perform outlier compensation on the wheel rotation measurements,measurements from the gyroscope, or measurements from the accelerometer,or any combination thereof. The instructions for performing outliercompensation on the wheel rotation measurements may compriseinstructions for determining values of staggered differences of thewheel rotation measurements. The instructions for determining thevehicle acceleration may comprise instructions for taking a median ofthe determined values of staggered differences. The non-transitorycomputer-readable medium may further comprise instructions that, whenexecuted by the one or more processing units, further cause the one ormore processing units to determine a vehicle velocity at least in partby using the estimated angular velocity and estimated linear velocityand determine a constant for each wheel rotation measurement, based atleast in part on the determined vehicle acceleration. The instructionsfor determining the vehicle velocity further include instructions forbasing the determining on a median of the constants for the wheelrotation measurements.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the disclosure are illustrated by way of example.

FIG. 1 is a simplified drawing providing an illustration of a scenarioin which accurate position determination may not be made on GNSS dataalone;

FIG. 2 is a block diagram of an on-vehicle position system, according toan embodiment;

FIG. 3 is an illustration of various coordinate frames with respect to avehicle, which may be utilized in performing the techniques foraccelerometer initialization described herein;

FIG. 4 is a flow diagram illustrating a method of robust accelerometerinitialization, according to an embodiment;

FIGS. 5A-5C are graphs used to illustrate an example of how robustestimates of the linear velocity and linear acceleration can be madefrom the rear wheel speed;

FIGS. 6A and 6B are charts comparing performance results of a case inwhich a VIO system uses the initialization techniques herein with a casein which the techniques are not used;

FIG. 7 is a flow diagram of a method of initializing an accelerometerlocated on a vehicle, according to an embodiment; and

FIG. 8 illustrates an embodiment of a mobile computing system, which maybe used to perform some or all of the functionality described in theembodiments herein.

Like reference symbols in the various drawings indicate like elements,in accordance with certain example implementations. In addition,multiple instances of an element may be indicated by following a firstnumber for the element with a letter or a hyphen and a second number.For example, multiple instances of an element 110 may be indicated as110-1, 110-2, 110-3 etc. or as 110 a, 110 b, 110 c, etc. When referringto such an element using only the first number, any instance of theelement is to be understood (e.g., element 110 in the previous examplewould refer to elements 110-1, 110-2, and 110-3 or to elements 110 a,110 b, and 110 c).

DETAILED DESCRIPTION

Several illustrative embodiments will now be described with respect tothe accompanying drawings, which form a part hereof. The ensuingdescription provides embodiment(s) only, and is not intended to limitthe scope, applicability or configuration of the disclosure. Rather, theensuing description of the embodiment(s) will provide those skilled inthe art with an enabling description for implementing an embodiment. Itis understood that various changes may be made in the function andarrangement of elements without departing from the spirit and scope ofthis disclosure.

It can be noted that, although embodiments described herein below aredirected toward determining the position of a vehicle, embodiments arenot so limited. Alternative embodiments, for example, may be directedtoward other mobile devices and/or applications in which alternativetypes of motion data indicative of acceleration and/or velocity (e.g.,other than wheel speed measurements, as described herein) may beutilized. A person of ordinary skill in the art will recognize manyvariations to the embodiments described herein.

Current navigation technology often utilizes GNSS-based positiondetermination of a vehicle (or a separate mobile device within thevehicle) to determine a route from the vehicle's current position to adesired destination. GNSS-based positioning is often accurate enough toprovide satisfactory navigation for such applications. However, for moreadvanced functionality, such as ADAS, semi-autonomous driving,fully-autonomous driving, and the like, highly accurate positiondetermination is imperative to ensure proper functionality. GNSS-basedpositioning alone can often fall short of providing sufficientlyaccurate position determination for such functionality, especially in“urban canyons” or similar scenarios in which a GNSS receiver of thevehicle may receive insufficient signals (not enough signals, signalswith insufficient signal strength, signals from non-line-of-sight (NLoS)satellites, etc.) from the GNSS system for accurate positiondetermination.

FIG. 1 is a simplified drawing providing an illustration of a scenarioin which accurate position determination may not be made on GNSS dataalone. Here, a vehicle 110 is driving in an urban environment 120 withmultiple tall buildings. Satellites 130-1 and 130-2 (collectivelyreferred to herein as “satellites 130”) may comprise satellite vehiclesof a GNSS system that provide wireless (e.g., radio frequency (RF))signals 140, 150 to a GNSS receiver on the vehicle 110 for determinationof the position (e.g., absolute or global coordinates) of the vehicle110. (Of course, although satellites 130 in FIG. 1 are illustrated asrelatively close to the urban environment 120 for visual simplicity, itwill be understood that satellites 130 will be in orbit around theearth. Moreover the satellites 130 may be part of a large constellationof satellites of a GNSS system. Additional satellites of such aconstellation are not shown in FIG. 1.)

Because the urban environment 120 has multiple tall buildings, it cancreate what is called an “urban canyon” that can make GNSS-basedpositioning of the vehicle 110 difficult in at least two ways. First,the buildings can block large portions of the sky, from the perspectiveof the vehicle 110, so that the GNSS receiver of the vehicle 110 isunable to receive signals from GNSS satellites in the blocked portionsof the sky. Second, because satellite signals can reflect off ofbuildings, the GNSS receiver of the vehicle 110 may receive indirectsignals 150 that can result in an inaccurate position determination.This phenomenon is illustrated in the scenario depicted in FIG. 1.

Thus, in “urban canyons” (as illustrated in FIG. 1) or similarscenarios, position determination from GNSS measurements alone may notbe accurate. A position determination function (e.g., executed inhardware and/or software of a computer system of the vehicle 110) mayneed to rely on data from other sensors of the vehicle 110 to helpimprove accuracy. Techniques described herein below are directed towardways in which one such sensor, an accelerometer, may be initialized tohelp provide accurate measurements for such position determination.

The above description notwithstanding, it can be noted that techniquesprovided herein may be used in a variety of applications andembodiments. As previously noted, techniques may be used in situations(as depicted in FIG. 1) where GNSS data may be incomplete or inaccurate.Additionally or alternatively, techniques provided herein may beutilized in embodiments in which position determination is whollydependent on non-GNSS a data. That is, the techniques described hereinbelow may be used to help ensure accuracy of accelerometer data in anyof a variety of embodiments, regardless of whether GNSS measurement datais available.

FIG. 2 is a block diagram of an on-vehicle position system 210,according to an embodiment, which may be used to employ some or all ofthe techniques for accelerometer initialization described herein below.The on-vehicle position system 210 collects data from various differentsources to determine and output a vehicle location and/or other positioninformation. The vehicle position can then be used in variousapplications and may be provided to other systems on the vehicle and/orsystems remote to the vehicle.

A person of ordinary skill in the art will understand that, inalternative embodiments, the components illustrated in FIG. 2 may becombined, separated, omitted, alternatively connected, and/or otherwisealtered, depending on desired functionality. Moreover, one or morecomponents of the on-vehicle position system 210 may be implemented inhardware, firmware, and/or software, such as the hardware, firmware,and/or software components of the mobile computing system 800illustrated in FIG. 8 and described in more detail below. These varioushardware, firmware, and/or software components may be distributed atvarious different locations on a vehicle, depending on desiredfunctionality

As previously mentioned, the on-vehicle position system 210 can collectdata from various sources to determine a position of the vehicle.Primary sources of data include the GNSS unit 225, camera(s) 220, andinertial measurement unit (IMU) 230. In alternative embodiments, theon-vehicle position system 210 may collect data from other sourcesadditionally or alternatively.

The GNSS unit 225 may comprise a GNSS receiver and GNSS processingcircuitry configured to receive signals from GNSS satellites andGNSS-based positioning data. The positioning data output by the GNSSunit 225 can vary, depending on desired functionality. In someembodiments, the GNSS unit 225 will provide, among other things, athree-degrees-of-freedom (3DoF) position determination (e.g., latitude,longitude, and altitude). Additionally or alternatively, the GNSS unit225 can output the underlying satellite measurements used to make the3DoF position determination. Additionally, or alternatively, the GNSSunit can output raw measurements, such as pseudo-range and carrier-phasemeasurements.

The camera(s) 220 may comprise one or more cameras disposed on or in thevehicle, configured to capture images, from the perspective of thevehicle, to help track movement of the vehicle. The camera(s) 220 may befront-facing, upward-facing, backward-facing, downward-facing, and/orotherwise positioned on the vehicle. Other aspects of the camera(s) 220,such as resolution, frame rate, optical band (e.g., visible light,infrared (IR), etc.), etc. may be determined based on desiredfunctionality. Movement of the vehicle may be tracked from imagescaptured by the camera(s) 220 using various image processing techniquesto determine motion blur, object tracking, and the like. The raw imagesand/or information resulting therefrom may be passed to a VIO engine,which can use the information, along with information received from theIMU 230 for position determination.

IMU 230 comprises one or more accelerometers 232, gyroscopes 234, and(optionally) other sensors 236, such as magnetometers, to provideinertial measurements. Similar to the camera(s) 220, the output of theIMU 230 to the VIO engine 240 may vary, depending on desiredfunctionality. In some embodiments, the output of the IMU 230 maycomprise information indicative of a three degrees-of-freedom (DoF)position or a 6DoF pose of the vehicle, and/or a 6DoF linear and angularvelocities of the vehicle, and may be provided periodically, based on aschedule, and/or in response to a triggering event. The positioninformation may be relative to an initial or reference position.Alternatively, the IMU may provide raw sensor measurements.

The VIO engine 240 may comprise a module (implemented in software and/orhardware) configured to combine input from the camera(s) 220 and IMU 230into a single output. For example, the different inputs may be givendifferent weights based on input type, a confidence metric (or otherindication of the reliability of the input), and the like. The output ofthe VIO engine 240 may comprise a 3DoF position and/or 6DoF pose basedon received inputs. This estimated position may be relative to aninitial or reference position, and may be synchronized to the output ofthe GNSS unit 225.

To provide the estimated position, the VIO engine 240 uses a navigationfilter to track estimates of several quantities (referred to as states).Usually these include vehicle pose and (linear) velocity. The navigationfilter may need to be initialized to ensure position determinationaccuracy. Initializing the navigation filter may involve initializingthe states of the navigation filter with coarse initial guesses forthese quantities. When initializing the navigation filter while thevehicle is static, this may be trivial for the velocity state, since itcan simply be set to zero. However, when initializing the navigationfilter while the vehicle is in motion, the initial value for thevelocity state may need to be more carefully chosen.

Similar to the VIO engine 240, the position determination unit 245 maycombine information received from multiple sources. Here, the positiondetermination unit 245 receives information from the GNSS unit 225 andthe VIO engine 240. It should be recognized that the positiondetermination unit 245 may receive information from other sources aswell, such as from a cellular base station, a wireless access point, orthe like. With this information, the position determination unit 245 canoutput a determined position (e.g., a 3DoF position or 6DoF pose) of thevehicle 110, relying on the output of VIO engine 240 to replace,complement, and/or enhance position determination of the GNSS unit 225,depending on desired functionality. In some embodiments, the VIO engine240 and position determination unit 245 may be combined (i.e., executedby the same software and/or hardware components of an on-vehicleposition system 210).

The output vehicle position may serve any of a variety of functions,depending on desired functionality. For example, it may be provided asan output of the on-vehicle position system 210 to other systems on thevehicle (and may be conveyed via a controller area network (CAN) bus,which is described in additional detail below), communicated to devicesseparate from the vehicle 110 (including other vehicles; serversmaintained by government agencies, service providers, and the like;etc.), shown on a display of the vehicle (e.g., to a driver or otheruser for navigation or other purposes), and the like.

Because the vehicle position determined by the position determinationunit 245 may be impacted by the output of the VIO engine 240, which, inturn is reliant on the accelerometer(s) 232 of the IMU 230, the accuracyof the data provided by the accelerometer(s) 232 is important in thedetermination of the vehicle position. And, as previously noted, properaccelerometer initialization, which accounts for accelerationmeasurements due to gravity, is important to ensure such accuracy.Traditional techniques involve conducting accelerometer initializationwhile the vehicle 110 is stationary. However, according to embodimentsdescribed herein, vehicle velocity and acceleration information may beused to allow for accelerometer and navigation filter initializationwhile the vehicle is in motion. According to some embodiments, the VIOengine 240 can obtain vehicle velocity and acceleration information froma CAN bus 250 and perform the accelerometer initialization as describedbelow. That said, a person of ordinary skill in the art will appreciatethat techniques may be employed by any of a variety of types of hardwareand/or software architectures.

FIG. 3 is an illustration of various coordinate frames with respect to avehicle 110, which may be utilized in performing the techniques foraccelerometer initialization described herein. In particular, the bframe 310 comprises a coordinate frame of an accelerometer body (e.g.,the body of an accelerometer 232 as illustrated in FIG. 2); the r frame320 comprises a coordinate frame of the vehicle body, located at therear axle of the vehicle; and the s frame 330 comprises a spatialcoordinate frame which represents a coordinate frame outside the vehicle110, which may be independent of the vehicle 110 (e.g., a coordinatesystem relative to the earth) and which may be the coordinate systemused by the GNSS unit 225. Because both of the b frame 310 and the rframe 320 are fixed, relative to the vehicle 110, the translation androtation between the b frame 310 and the r frame 320 (although it may bevehicle-dependent) is constant and may therefore be known for anyparticular vehicle and/or vehicle type. Thus, variables defined in onecoordinate frame may be translated to the other.

FIG. 4 is a flow diagram illustrating a method 400 of robustaccelerometer initialization, according to an embodiment. Morespecifically, the method of 400 allows for the determination of andcompensation for the gravitational acceleration and velocity (if any) onthe accelerometer. As with other figures provided herein, FIG. 4 isprovided as a non-limiting example. Alternative embodiments may performfunctions in alternative order, combine, separate, and/or rearrange thefunctions illustrated in the blocks of FIG. 4, and/or perform functionsin parallel, depending on desired functionality. A person of ordinaryskill in the art will appreciate such variations. The functions of oneor more blocks illustrated in FIG. 4 may be performed by a processingunit communicatively coupled with a CAN, gyroscope, and accelerometer.Such a processing unit may be part of an on-vehicle position system(e.g., part of a VIO engine 240 and/or position determination unit 245of on-vehicle position system 210, as illustrated in FIG. 2) and/ormobile computing system (e.g., mobile computing system 800, asillustrated in FIG. 8).

At block 410, CAN, gyroscope, and accelerometer data are collected. Toenable the determination of acceleration from velocity data, multiplevelocity measurements may be captured over a period of time. This timewindow may vary, depending on desired functionality. In someembodiments, this window of time is long enough to ensure multiplemeasurements are captured, while short enough to assume angular velocityis roughly constant. In some embodiments, gyroscope and accelerometermeasurements may be captured at approximately 200 Hz, and rear wheelspeed measurements from the CAN may be captured at approximately 100 Hz,although alternative embodiments may capture data at different rates. Insuch embodiments, the period of time for data collection may be lessthan one second (e.g., 0.75, 0.5, 0.25 seconds, etc.).

At block 420, the CAN data is used to estimate the vehicle's linearvelocity and acceleration with respect to the r frame. As noted above,the CAN data may comprise wheel speed data (or other data indicative oflinear velocity), which can be used to determine the vehicle's linearvelocity and acceleration. Depending on the period of time over whichdata is collected and the data sampling rate, multiple data samples maybe used to make this determination. In instances where the wheel speeddata is sampled at a rate of 100 Hz, for example, 50 wheel speedmeasurements may be obtained over a period of 0.5 seconds.

Estimation of the vehicle's linear velocity and acceleration may utilizethe mathematical formulas that follow. However, it can be noted that,with respect to the embodiments herein and the following formulas,although coordinate frames are expressed in

³, the techniques herein may be applied using alternative coordinatesystems.

As a matter of notation, for frames x, y, and z, ^(z)T_(xy)∈

³ denotes the translation from x to y expressed in z coordinates. Theshorthand T_(xy)

^(x)T_(xy) is also used herein. Similarly, R_(xy) is the rotation from xto y. Furthermore, V denotes (linear) velocity, ω denotes angularvelocity, and a denotes (linear) acceleration, each of which is in

³. The following formula can be used to relate translations in differentframes:

T _(xy) =T _(xz)+^(x) T _(zy) =T _(xz) +R _(xz) T _(zy)  (1)

Using this notation, then, the functionality of block 420 of FIG. 4 mayinclude estimating linear velocity, ^(r)V_(sr)(t), and acceleration,

${\frac{d}{dt}{{{}_{}^{}{}_{}^{}}(t)}},$

from rear wheel speed measurements and/or another indicator of linearvelocity. Because ^(r)V_(sr)(t) is always pointing forward or backwardalong they axis in the r frame (see FIG. 3), only the second element ofa vector describing velocity in 3 dimensions with respect to the r framewill be non-zero. Consequently, the same applies to

$\frac{d}{dt}{{{{}_{}^{}{}_{}^{}}(t)}.}$

Thus, the estimation of ^(r)V_(sr)(t) is of the form

(0,{circumflex over (V)},0)^(T)  (2)

and the estimation of

$\frac{d}{dt}{{{}_{}^{}{}_{}^{}}(t)}$

is of the form

(0,{circumflex over (α)},0)^(T).  (3)

Let V^((k)), k=0, 2, 3, . . . , N−1 be N rear wheel speed samples withtimestamps t^((k)), k=0, 2, 3, . . . , N−1. Each sample V^((k)) may be ameasured speed of a rear wheel at time t^((k)). In some embodiments,each sample V^((k)) may be an average of the measured speeds of two rearwheels at time t^((k)). Where acceleration is assumed to be constantover the period of time in which measurements were made (at block 410),{circumflex over (α)} can be computed as the slope of the speed samples.

According to some embodiments, variables {circumflex over (V)} and/or{circumflex over (α)} may be computed to account for outliers in theV^((k)) speed samples, to allow for a robust estimate of linear velocityand acceleration. Examples of such techniques are provided herein below.

Referring again to FIG. 4, the functionality at block 430 comprisesestimating the vehicle's angular velocity with respect to the b frameusing gyroscope data. Here, angular velocity, ^(b)ω_(sb), may be assumedto be constant for the period of time during which gyroscopemeasurements were made (at block 410), in a manner similar to the valuesestimated at block 420.

In some embodiments, the gyroscope may share the same frame (the bframe) of the accelerometer. (This may occur, for example, in instanceswhere the accelerometer and gyroscope are part of the same IMU, asillustrated in FIG. 2. If this does not occur, the measurements can betransformed to the accelerometer frame.) In such instances, let y_(gyr)^((k)), k=0, 2, 3, . . . , N_(g)−1 be N_(g) gyroscope samples. Eachsample y_(gyr) ^((k)) may be a measure of angular velocity of thegyroscope at time t^((k)). Estimates of the angular velocity based onthe samples may account for gyroscope imperfections (e.g., bias,scaling, and non-orthogonality). Moreover, similar to estimates oflinear velocity and acceleration as described above, estimates ofangular velocity may further account for outliers in the gyroscopesamples. Again, examples of such techniques are provided herein below.

At block 440, accelerometer data is used to estimate total acceleration,or specific force, with respect to the b frame. (As used herein, theterms “total acceleration” and “specific force” are usedinterchangeably, and refer to the total of all acceleration componentsacting on the accelerometer. As noted below, the accelerometer'smeasurements of this value may include some error due to bias, scaling,etc.) Here, specific force, ^(b)f_(sb), may be assumed to be constantfor the period of time during which accelerometer measurements were made(at block 410), in a manner similar to the values estimated at blocks420 and 430.

The estimation of the specific force at block 440 may be similar to theestimation of the vehicle's angular velocity in block 430. Let y_(acc)^((k)), k=0, 2, 3, . . . , N_(a)−1 be N_(a) gyroscope samples. Eachsample y_(acc) ^((k)) may be a measure of acceleration by theaccelerometer at time t^((k)). Similar to estimates of the angularvelocity, estimates of total acceleration can take into accountimperfections of the accelerometer (e.g., bias, scaling, andnon-orthogonality). Moreover, estimates of acceleration may furtheraccount for outliers in the accelerometer samples. Again, examples ofsuch techniques are provided herein below.

At block 450, the vehicle's acceleration with respect to the b frame iscomputed using the vehicle's angular velocity, linear velocity, andacceleration, as estimated at block 420-440. Because linear velocity andacceleration calculated in block 420 is with respect to the r frame, thevehicle acceleration computed with respect to the b frame involvestranslating and rotating data from the r frame to the b frame. However,as noted above, the b frame and r frame are fixed with respect to eachother. And thus, the translation T_(rb) and rotation R_(rb) are known orcan be determined. As such, the vehicle's acceleration ^(b)α_(sb), maybe determined using the following equation:

$\begin{matrix}{{{}_{}^{}{}_{sb}^{}} = {R_{rb}^{T}\left( {{\frac{d}{dt}{{}_{}^{}{}_{}^{}}} + {\left\lbrack {\left( {R_{rb}^{b}\omega_{sb}} \right) \times} \right\rbrack^{r}V_{sr}} + {{\left\lbrack {\left( {R_{rb}^{b}\omega_{sb}} \right) \times} \right\rbrack \left\lbrack {\left( {R_{rb}^{b}\omega_{sb}} \right) \times} \right\rbrack}T_{rb}}} \right)}} & (4)\end{matrix}$

At block 460, gravitational acceleration in the b frame can then becomputed using the estimated total acceleration and computed vehicleacceleration. Specific force estimated at block 440, may be described asvehicle acceleration minus gravitational acceleration. In other words,in the b frame, specific force, ^(b)f_(sb), may be defined as follows:

^(b) f _(sb)

^(b)α_(sb)−^(b)γ  (5)

where ^(b)γ is the gravity in the b frame. Therefore, gravitationalacceleration may be calculated as

^(b)γ=^(b)α_(sb)−^(b) f _(sb).   (6)

At block 470, velocity in the b frame can be computed using the angularvelocity and linear velocity, estimated at blocks 430 and 420,respectively. Because the estimated linear velocity, ^(r)V_(sr), is withrespect to the r frame, it needs to be translated and rotatedaccordingly. Accordingly, velocity in the b frame, ^(b)V_(sb), may becomputed as follows:

^(b) V _(sb) =R _(rb) ^(T)(^(r) V _(sr)[(R _(rb) ^(b)ω_(sb))×]T_(rb)).  (7)

It can be noted that, because the computation of velocity in block 470does not depend on the computation of gravity at block 460, the order inwhich gravity and velocity are computed may vary, depending on desiredfunctionality. In some embodiments, velocity may be computed aftergravity (as shown in FIG. 4). In other embodiments, velocity may becomputed before or at the same time as gravity. A person of ordinaryskill in the art will appreciate that the order of other functionsillustrated in FIG. 4 may be similarly rearranged.

As previously mentioned, measurements from the accelerometer, gyroscope,and CAN may include outlier values that, if unaccounted for, may resultin an inaccurate or less accurate estimate of angular velocity, linearvelocity, and/or acceleration based on these measurements. With this inmind, embodiments may account for such values (that is, perform outliercompensation) in any of a variety of ways, depending on desiredfunctionality.

For example, for the estimation of linear acceleration at block 420 ofFIG. 4, the value of {circumflex over (α)} in Expression (3) aboveshould be the slope of the speed samples. According to some embodiments,the value of {circumflex over (α)} may be computed by taking staggereddifferences of speed samples (e.g., for a sample set of 100 samples, thedifference between samples 1 and 51, samples 2 and 52, sample 3 and 53,and so on), then taking the median of those differences. In other words,the value of {circumflex over (α)} may be computed as

$\begin{matrix}{\overset{\hat{}}{\alpha}\overset{\bigtriangleup}{=}{{median}\mspace{11mu} {\left\{ {\frac{V^{({i + n})} - V^{(i)}}{t^{({i + n})} - t^{(i)}},\ {i = 0},1,2,\ldots \mspace{11mu},\ {N - 1}} \right\}.}}} & (8)\end{matrix}$

By taking staggered differences and performing a median in this manner,the effect of outlier values is reduced. As such, this process may beconsidered a means of outlier compensation.

With regard to the estimation of linear velocity, it may not bedesirable to estimate the value of V in Expression (2) above asV^((N-1)) because V^((N-1)) may be an outlier. The speed samples may bemodeled as

V ^((k)) ≈{circumflex over (α)}t ^((k)) +c  (9)

where c is a constant. To find a robust value for {circumflex over (V)},a robust estimation of c may be first obtained with, for example, thefollowing equation:

ĉ

median{V ^((k)) −{circumflex over (α)}t ^((k)) ,k=0,1,2, . . .,N−1}  (10)

From (9) and (10), we can then compute V as

{circumflex over (V)}

{circumflex over (α)}t ^((k)) +ĉ.  (11)

According to some embodiments, the estimation of the vehicle's angularvelocity at block 430 also may use the median of obtained measurementsamples. For measurement samples y_(gyr) ^((k)), k=0, 2, 3, . . . ,N_(g)−1 a median may be obtained by

ŷ _(gyr)

median{y _(gyr) ^((k)) ,k=0,2,3, . . . ,N _(g)−1}.  (12)

As previously noted, the value of y_(gyr) ^((k)) may be affected bynoise, bias, and/or other imperfections. A robust estimation of angularvelocity, ^(b)ω_(sb), may then be calculated by

^(b){circumflex over (ω)}_(sb)

A _(g) ⁻¹(ŷ _(gyr) −b _(g))  (13)

where A_(g) is a matrix containing the scale factors andnon-orthogonalities and b_(g) is a bias term, both assumed to beconstant and known.

The robust estimation of the specific force at block 440 of FIG. 4 maybe similarly based on a median, according to some embodiments. Forexample, a robust estimation of ^(b)f_(sb) may be determined by may beobtained by

^(b) {circumflex over (f)} _(sb)

A _(a) ⁻¹(median{y _(acc) ^((k)) ,k=0,2,3, . . . ,N _(a)−1}−b_(a))  (14)

where A_(a) is a matrix containing the scale factors andnon-orthogonalities and b_(a) is a bias term, both assumed to beconstant and known. Here again, the value of y_(acc) ^((k)) may beaffected by noise, bias, and/or other imperfections.

FIGS. 5A-5C are graphs used to illustrate an example of how robustestimates of the linear velocity and linear acceleration can be madefrom the rear wheel speed. As noted elsewhere herein, embodiments arenecessarily limited to rear wheel speed, and may use alternative dataindicative of linear velocity, if available.

FIG. 5A is a graph illustrating CAN measurements of the rear wheel speedobtained over a period of 0.5 seconds. In this example, speedmeasurements occur at 100 Hz, thereby resulting in 50 samples (att=0.01, 0.02, . . . , 0.5). Here, the acceleration, a, is 3 m/s², andrear wheel speed is 10+ta (m/s). As can be seen, measurements V(t) haveoutliers 510-1, 510-2, and 510-3 occur at time 0.1, 0.18, and 0.39,respectively. As indicated previously, the difference of offset valuesmay be used to determine an acceleration from the CAN rear wheel speedmeasurements.

FIG. 5B is a graph illustrating acceleration values derived from the CANmeasurements of FIG. 5A using Equation (8) above. That is, first pair ofoffset values 520-1 (FIG. 5A) is used to determine a first accelerationvalue 530-1 (FIG. 5B), a second pair of offset values 520-2 is used todetermine a second acceleration value 530-2, and so forth. Although theembodiments herein use values offset by half the length of the period oftime (here, values offset by 0.25 seconds) alternative embodiments mayuse pair of values differently for acceleration determination.

In accordance with Equation (8), the median of the acceleration samplesof FIG. 5B is used to provide the robust estimate of the acceleration.This can take care of outliers in the acceleration samples of FIG. 5B(which are result of outliers 510 in the underlying measurement samplesof FIG. 5A). In this case, the median is determined to be 2.9718 m/s²,which, despite the outliers, is very close to the true value of 3 m/s².

FIG. 5C is a graph illustrating how illustrates constant samples areobtained from the data in FIGS. 5A and 5B. Using the relationshipbetween the constant, velocity, and acceleration for any given time (asprovided in Expression (9) above), and assuming constant acceleration,the constant term of speed can be calculated for measurement sample ofFIG. 5A. Here, the constant acceleration has been calculated as 2.9718m/s², and thus each constant term of speed can be calculated asV(t)−2.9718t. According to Equation (10) above, the median is 10.004m/s, which is very close to the true value of 10 m/s. Using Equation(11) above, the velocity at t=0.5, is calculated as2.9718*0.5+10.004=11.49 m/s.

FIGS. 6A and 6B are charts comparing performance results of a case inwhich a VIO system uses the initialization techniques herein with a casein which the techniques are not used. All the results here are based onmeasurement data collected in real test drives. They axes of the chartsrepresent horizontal error (distance from true position in a horizontalplane), in meters; the x axes of the charts represent duration of thetest drive, in seconds.

FIG. 6A is a chart plotting positioning error when the VIO system isinitialized when the vehicle is traveling at a velocity of 10 m/s andexperiencing an acceleration of approximately 3 m/s². Plot 610illustrates the error when the techniques herein are not used (i.e., thesystem initializes as if the vehicle is stationary). As can be seen, thepositioning error quickly diverges, ultimately plateauing at over akilometer of horizontal error. Such divergence makes the VIO systemunusable after a matter of seconds.

On the other hand, plot 620 illustrates the error when the techniquesherein are used. As can be seen, the VIO system remains highly accurate,remaining at or within a meter of accuracy for a majority of the time.

FIG. 6B is a chart plotting positioning error for a different test drivethan that of FIG. 6A. Here, the VIO system is initialized when thevehicle is traveling at a velocity of 26 m/s and experiencing anacceleration of approximately 0.8 m/s². (That is, the test drive of FIG.6B has higher velocity and lower acceleration during VIO initializationthan the test drive of FIG. 6A.) Here, plot 630 illustrates the errorwhen the initialization techniques herein are not used, and plot 640illustrates the error when the techniques are used. As can be seen, plot630 diverges even more quickly and results in even larger error thanplot 610 of FIG. 6A. On the other hand, plot 640 (similar to plot 620)illustrates how the VIO system remains highly accurate, with accuracy ofless than one meter for a majority of the drive.

FIG. 7 is a flow diagram of a method 700 of initializing anaccelerometer located on a vehicle, according to an embodiment. Here, aswith FIG. 4, the method allows for the determination of the effects ofgravity and velocity on an accelerometer. In some ways, the method 700of FIG. 7 may be considered a more generalized version of the method 400of FIG. 4. Alternative embodiments may perform functions in alternativeorder, combine, separate, and/or rearrange the functions illustrated inthe blocks of FIG. 7, and/or perform functions in parallel, depending ondesired functionality. A person of ordinary skill in the art willappreciate such variations. Means for performing the functionality ofone or more blocks illustrated in FIG. 7 include a processing unitand/or other hardware and/or software components of a mobile computersystem, such as the mobile computing system 800 of FIG. 8, described infurther detail below. Additionally or alternatively, such means mayinclude specialized hardware as described in relation to FIG. 2.

At block 710, a total acceleration or specific force is estimated basedon measurements from the accelerometer taken during a period of time. Asnoted in the embodiments described above, imperfections in theaccelerometer data, such as non-orthogonality, bias, and scaling may beaccounted for to help ensure accuracy in the acceleration estimation. Asfurther noted above, the period of time may vary, depending on sensorsampling rates and/or other factors. Means for performing thefunctionality of block 710 may include a bus 805, processing unit(s)810, input device(s) 815, and/or other components of a mobile computingsystem 800 as illustrated in FIG. 8 and described in further detailbelow.

At block 720, the functionality includes estimating a linear velocityand an acceleration based on wheel rotation measurements of the vehicletaken during the period of time. As indicated in the embodiments above,wheel rotation measurements may be received from the CAN bus and mayinclude, for example, wheel speed measurements and/or wheel tickinformation. The wheel rotation measurements themselves, or velocity (orother data) derived therefrom, then may be provided to a processing unit(or other hardware and/or software component) performing thefunctionality of block 720, according to some embodiments. This can beperformed, for example, using the particular mathematical formulasprovided herein. More generally, this functionality may be performed bydetermining displacement over time (which may include determining acircumference of one or more wheels for which the wheel rotationmeasurements are made) to determine velocity, and determining a slope ofthe displacement (e.g., taking a derivative) to determine exhilaration.As previously noted, estimating the linear velocity and acceleration mayfurther comprise translating the wheel rotation measurements from afirst coordinate frame to a second coordinate frame. Means forperforming the functionality of block 720 may include a bus 805,processing unit(s) 810, input device(s) 815, and/or other components ofa mobile computing system 800 as illustrated in FIG. 8 and described infurther detail below.

At block 730, an angular velocity is estimated, based on measurementsfrom a gyroscope located on the vehicle and taken during the period oftime. Similar to the estimate of total acceleration based on themeasurements from the accelerometer, the estimate of angular velocitybased on measurements from the gyroscope may also take into accountimperfections of the gyroscope, such as non-orthogonality, bias, andscale, to help ensure accuracy of the estimate. In some embodiments, thegyroscope and accelerometer may compose at least a portion of an IMU. Insuch embodiments, the IMU may compose a portion of a VIO system. Meansfor performing the functionality of block 730 may include a bus 805,processing unit(s) 810, input device(s) 815, and/or other components ofa mobile computing system 800 as illustrated in FIG. 8 and described infurther detail below.

At block 740, vehicle acceleration is determined based on the estimatedlinear velocity, estimated total acceleration, and estimated angularvelocity. As noted above, because linear velocity and acceleration maybe determined in a different coordinate frame (e.g., a coordinate framecentered on the rear axle of the vehicle) than the coordinate frame forwhich angular velocity is determined (e.g., the coordinate frame of thegyroscope or IMU comprising the gyroscope), one or more of the estimatedlinear velocity, estimated acceleration, and/or estimated angularvelocity may be translated and rotated to ensure a common coordinateframe. Means for performing the functionality of block 740 may include abus 805, processing unit(s) 810, and/or other components of a mobilecomputing system 800 as illustrated in FIG. 8 and described in furtherdetail below.

At block 750, gravitational acceleration is determined, based on theestimated total acceleration and the determined vehicle acceleration.Put simply, the gravitational acceleration may comprise the accelerationmeasured by the accelerometer due to gravity, and may be determined bysubtracting the determined vehicle acceleration from the estimated totalacceleration. Means for performing the functionality of block 750 mayinclude a bus 805, processing unit(s) 810, and/or other components of amobile computing system 800 as illustrated in FIG. 8 and described infurther detail below.

At block 760, a processing unit located on the vehicle offsets asubsequent measurement of the accelerometer based on the determinedgravitational acceleration measurement. Here, the processing unit maycomprise one or more processing units also used in any of the functionsperformed at block 710-750. In some embodiments, the offset subsequentmeasurement may be used in an/or provided to a position determinationsystem, for determination of the location of the vehicle. As noted inprevious embodiments, such a system may utilize additional sources ofinformation, such as a GNSS receiver. In some embodiments, the method700 may further comprise estimating, with the processing unit, alocation of the vehicle based at least in part on the offset subsequentmeasurement of the accelerometer. Means for performing the functionalityof block 760 may include a bus 805, processing unit(s) 810, and/or othercomponents of a mobile computing system 800 as illustrated in FIG. 8 anddescribed in further detail below.

As previously indicated, a vehicle velocity optionally may be determinedusing the estimated angular velocity and estimated linear velocity.While the techniques for determining gravitational acceleration providedherein enable initialization of the accelerometer will the vehicle is inmotion, determining a vehicle velocity can enable proper initializationof a navigation filter while the vehicle is in motion. As describedabove, a navigation filter may enable accurate position determination bytracking estimates of several quantities, including vehicle pose and(linear) velocity. As such, in embodiments in which a navigation filteris utilized, the vehicle velocity may be determined.

The determination of the vehicle velocity may require translation inrotation of the estimated angular velocity and/or estimated linearvelocity to a common reference frame. In some embodiments, such atranslation and rotation may have been made at block 740 during thedetermination of vehicle acceleration.

As indicated in the embodiments described above, out outlier values inthe wheel rotation measurements, measurements from the accelerometer,and/or measurements from the gyroscope, and/or data derived therefrom,may be compensated for in any of a variety of ways. For example, outliercompensation may be performed on wheel rotation measurements, asdescribed in relation to FIGS. 5A and 5B, by determining values ofstaggered differences of the wheel rotation measurements. In someembodiments, outlier compensation may further comprise taking a medianof the determined values of staggered differences. As noted above, usingstaggered differences (and optionally the median thereof) in this manneris a means of outlier compensation due to the fact that this techniquereduces the effects of outliers. As described in relation to FIG. 5C,determining the vehicle velocity may comprise determining a constant foreach wheel rotation measurement, based at least in part on thedetermined vehicle acceleration. Embodiments may additionally determinethe vehicle velocity further based on a median of the constants for eachwheel rotation measurement.

FIG. 8 illustrates an embodiment of an mobile computing system 800,which may be used to perform some or all of the functionality describedin the embodiments herein, including the functionality of one or more ofthe blocks illustrated in FIGS. 4 and 7. The mobile computing system 800may be located on a vehicle, and may incorporate or be incorporated intothe on-vehicle position system 210 of FIG. 2. For example, the VIOengine 240 and/or position determination unit 245 of FIG. 2 may beexecuted by processing unit(s) 810, the IMU 230 may be included in theinput device(s) 815, the GNSS unit 225, and so forth. A person ofordinary skill in the art will appreciate where additional oralternative components of FIG. 2 and FIG. 8 may overlap.

It should be noted that FIG. 8 is meant only to provide a generalizedillustration of various components, any or all of which may be utilizedas appropriate. FIG. 8, therefore, broadly illustrates how individualsystem elements may be implemented in a relatively separated orrelatively more integrated manner. In addition, it can be noted thatcomponents illustrated by FIG. 8 can be localized to a single deviceand/or distributed among various networked devices, which may bedisposed at different physical locations on a vehicle.

The mobile computing system 800 is shown comprising hardware elementsthat can be communicatively coupled via a bus 805 (or may otherwise bein communication, as appropriate). The hardware elements may includeprocessing unit(s) 810, which can include without limitation one or moregeneral-purpose processors, one or more special-purpose processors (suchas a digital signal processor (DSP), graphical processing unit (GPU),application specific integrated circuit (ASIC), field-programmable gatearray (FPGA), and/or the like), and/or other processing structure, whichcan be configured to perform one or more of the methods describedherein, including at least a portion of the methods described in FIGS. 4and 7. The mobile computing system 800 also can include one or moreinput devices 815, which can include without limitation a CAN bus(and/or another source of data for various vehicle systems), IMU (and/orone or more accelerometers, gyroscopes, etc.), camera, GNSS receiver,and/or the like; and one or more output devices 820, which can includewithout limitation a display device, one or more vehicle automationand/or control systems, and/or the like.

The mobile computing system 800 may further include (and/or be incommunication with) one or more non-transitory storage devices 825,which can comprise, without limitation, local and/or network accessiblestorage, and/or can include, without limitation, a disk drive, a drivearray, an optical storage device, a solid-state storage device, such asa random access memory (“RAM”), and/or a read-only memory (“ROM”), whichcan be programmable, flash-updateable, and/or the like. Such storagedevices may be configured to implement any appropriate data stores,including without limitation, various file systems, database structures,and/or the like.

The mobile computing system 800 may also include a communicationssubsystem 830, which can include support of communication technologies,including wireless communication technologies managed and controlled bya wireless communication interface 833. The communications subsystem 830may include a modem, a network card (wireless or wired), an infraredcommunication device, a wireless communication device, and/or a chipset,and/or the like. The communications subsystem 830 may include one ormore input and/or output communication interfaces, such as the wirelesscommunication interface 833, to permit data to be exchanged with awireless network, mobile devices, other computer systems, and/or otherelectronic devices. A wireless network may include a cellular network orother wireless wide area network (WWAN), a wireless local area network(WLAN), and/or the like.

In many embodiments, the mobile computing system 800 will furthercomprise a working memory 835, which can include a RAM and/or ROMdevice. Software elements, shown as being located within the workingmemory 835, can include an operating system 840, device drivers,executable libraries, and/or other code, such as application(s) 845,which may comprise computer programs provided by various embodiments,and/or may be designed to implement methods, and/or configure systems,provided by other embodiments, as described herein. Merely by way ofexample, one or more procedures described with respect to the method(s)discussed above, such as the methods described in relation to FIGS. 4and 7, may be implemented (in whole or in part) as code and/orinstructions that are stored (e.g. temporarily) in working memory 835and are executable by a computer (and/or a processing unit within acomputer such as processing unit(s) 810); in an aspect, then, such codeand/or instructions can be used to configure and/or adapt a generalpurpose computer (or other device) to perform one or more operations inaccordance with the described methods.

A set of these instructions and/or code might be stored on anon-transitory computer-readable storage medium, such as the storagedevice(s) 825 described above. In some cases, the storage medium mightbe incorporated within a computer system, such as mobile computingsystem 800. In other embodiments, the storage medium might be separatefrom a computer system (e.g., a removable medium, such as an opticaldisc), and/or provided in an installation package, such that the storagemedium can be used to program, configure, and/or adapt a general purposecomputer with the instructions/code stored thereon. These instructionsmight take the form of executable code, which is executable by themobile computing system 800 and/or might take the form of source and/orinstallable code, which, upon compilation and/or installation on themobile computing system 800 (e.g., using any of a variety of generallyavailable compilers, installation programs, compression/decompressionutilities, etc.), then takes the form of executable code.

It will be apparent to those skilled in the art that substantialvariations may be made in accordance with specific requirements. Forexample, customized hardware might also be used, and/or particularelements might be implemented in hardware, software (including portablesoftware, such as applets, etc.), or both. Further, connection to othercomputing devices such as network input/output devices may be employed.

With reference to the appended figures, components that can includememory can include non-transitory machine-readable media. The term“machine-readable medium” and “computer-readable medium” as used herein,refer to any storage medium that participates in providing data thatcauses a machine to operate in a specific fashion. In embodimentsprovided hereinabove, various machine-readable media might be involvedin providing instructions/code to processing units and/or otherdevice(s) for execution. Additionally or alternatively, themachine-readable media might be used to store and/or carry suchinstructions/code. In many implementations, a computer-readable mediumis a physical and/or tangible storage medium. Such a medium may takemany forms, including but not limited to, non-volatile media, volatilemedia, and transmission media. Common forms of computer-readable mediainclude, for example, magnetic and/or optical media, any other physicalmedium with patterns of holes, a RAM, a PROM, EPROM, a FLASH-EPROM, anyother memory chip or cartridge, a carrier wave as described hereinafter,or any other medium from which a computer can read instructions and/orcode.

The methods, systems, and devices discussed herein are examples. Variousembodiments may omit, substitute, or add various procedures orcomponents as appropriate. For instance, features described with respectto certain embodiments may be combined in various other embodiments.Different aspects and elements of the embodiments may be combined in asimilar manner. The various components of the figures provided hereincan be embodied in hardware and/or software. Also, technology evolvesand, thus, many of the elements are examples that do not limit the scopeof the disclosure to those specific examples.

It has proven convenient at times, principally for reasons of commonusage, to refer to such signals as bits, information, values, elements,symbols, characters, variables, terms, numbers, numerals, or the like.It should be understood, however, that all of these or similar terms areto be associated with appropriate physical quantities and are merelyconvenient labels. Unless specifically stated otherwise, as is apparentfrom the discussion above, it is appreciated that throughout thisSpecification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining,” “ascertaining,”“identifying,” “associating,” “measuring,” “performing,” or the likerefer to actions or processes of a specific apparatus, such as a specialpurpose computer or a similar special purpose electronic computingdevice. In the context of this Specification, therefore, a specialpurpose computer or a similar special purpose electronic computingdevice is capable of manipulating or transforming signals, typicallyrepresented as physical electronic, electrical, or magnetic quantitieswithin memories, registers, or other information storage devices,transmission devices, or display devices of the special purpose computeror similar special purpose electronic computing device.

Terms, “and” and “or” as used herein, may include a variety of meaningsthat also is expected to depend at least in part upon the context inwhich such terms are used. Typically, “or” if used to associate a list,such as A, B, or C, is intended to mean A, B, and C, here used in theinclusive sense, as well as A, B, or C, here used in the exclusivesense. In addition, the term “one or more” as used herein may be used todescribe any feature, structure, or characteristic in the singular ormay be used to describe some combination of features, structures, orcharacteristics. However, it should be noted that this is merely anillustrative example and claimed subject matter is not limited to thisexample. Furthermore, the term “at least one of” if used to associate alist, such as A, B, or C, can be interpreted to mean any combination ofA, B, and/or C, such as A, AB, AA, AAB, AABBCCC, etc.

Having described several embodiments, various modifications, alternativeconstructions, and equivalents may be used without departing from thespirit of the disclosure. For example, the above elements may merely bea component of a larger system, wherein other rules may take precedenceover or otherwise modify the application of the various embodiments.Also, a number of steps may be undertaken before, during, or after theabove elements are considered. Accordingly, the above description doesnot limit the scope of the disclosure.

What is claimed is:
 1. A method of initializing an accelerometer locatedon a vehicle, the method comprising: estimating a total accelerationbased on measurements from the accelerometer taken during a period oftime; estimating a linear velocity and an acceleration based on wheelrotation measurements of the vehicle taken during the period of time;estimating an angular velocity based on measurements from a gyroscopelocated on the vehicle and taken during the period of time; determiningvehicle acceleration based on the estimated linear velocity, estimatedtotal acceleration, and estimated angular velocity; determininggravitational acceleration based on the estimated total acceleration anddetermined vehicle acceleration; and offsetting, with a processing unitlocated on the vehicle, a subsequent measurement of the accelerometerbased on the determined gravitational acceleration.
 2. The method ofclaim 1, further comprising: determining a vehicle velocity using theestimated angular velocity and estimated linear velocity; and using thedetermined vehicle velocity to initialize a navigation filter.
 3. Themethod of claim 1, wherein estimating the linear velocity and estimatingthe total acceleration further comprises translating the wheel rotationmeasurements from a first coordinate frame to a second coordinate frame.4. The method of claim 1, wherein the accelerometer and the gyroscopecompose at least a portion of an inertial measurement unit (IMU) locatedon the vehicle.
 5. The method of claim 4, wherein the IMU composes aportion of a visual inertial odometry (VIO) system located on thevehicle.
 6. The method of claim 1, further comprising estimating, withthe processing unit, a location of the vehicle based at least in part onthe offset subsequent measurement of the accelerometer.
 7. The method ofclaim 1, further comprising performing outlier compensation on the wheelrotation measurements, measurements from the gyroscope, or measurementsfrom the accelerometer, or any combination thereof.
 8. The method ofclaim 7, comprising performing outlier compensation on the wheelrotation measurements, wherein performing the outlier compensationcomprises determining values of staggered differences of the wheelrotation measurements.
 9. The method of claim 8, wherein determining thevehicle acceleration comprises taking a median of the determined valuesof staggered differences.
 10. The method of claim 9, further comprisingdetermining a vehicle velocity at least in part by using the estimatedangular velocity and estimated linear velocity and determining aconstant for each wheel rotation measurement, based at least in part onthe determined vehicle acceleration.
 11. The method of claim 10, whereindetermining the vehicle velocity further is based on a median of theconstants for the wheel rotation measurements.
 12. A mobile computingsystem comprising: a memory; and a processing unit communicativelycoupled with the memory and configured to: estimate a total accelerationbased on measurements received from an accelerometer located on avehicle and taken during a period of time; estimate a linear velocityand an acceleration based on wheel rotation measurements of the vehicletaken during the period of time; estimate an angular velocity based onmeasurements from a gyroscope located on the vehicle and taken duringthe period of time; determine vehicle acceleration based on theestimated linear velocity, estimated total acceleration, and estimatedangular velocity; determine gravitational acceleration based on theestimated total acceleration and determined vehicle acceleration; andoffset a subsequent measurement of the accelerometer based on thedetermined gravitational acceleration.
 13. The mobile computing systemof claim 12, wherein the processing unit is further configured to:determine a vehicle velocity using the estimated angular velocity andestimated linear velocity; and use the determined vehicle velocity toinitialize a navigation filter.
 14. The mobile computing system of claim12, wherein the processing unit is configured to estimate the linearvelocity and configured to estimate the total acceleration at least inpart by translating the wheel rotation measurements from a firstcoordinate frame to a second coordinate frame.
 15. The mobile computingsystem of claim 12, further comprising an IMU comprising theaccelerometer and the gyroscope.
 16. The mobile computing system ofclaim 15 further comprising a visual inertial odometry (VIO) system,which comprises the IMU.
 17. The mobile computing system of claim 12,wherein the processing unit is configured to estimate a location of thevehicle based at least in part on the offset subsequent measurement ofthe accelerometer.
 18. The mobile computing system of claim 12, whereinthe processing unit is configured to perform outlier compensation on thewheel rotation measurements, measurements from the gyroscope, ormeasurements from the accelerometer, or any combination thereof.
 19. Themobile computing system of claim 18, wherein the processing unit isconfigured to perform the outlier compensation at least in part bydetermining values of staggered differences of the wheel rotationmeasurements.
 20. The mobile computing system of claim 19, wherein theprocessing unit is configured to determine the vehicle acceleration atleast in part by taking a median of the determined values of staggereddifferences.
 21. The mobile computing system of claim 20, wherein theprocessing unit is further configured to determine a vehicle velocity atleast in part by using the estimated angular velocity and estimatedlinear velocity and determining a constant for each wheel rotationmeasurement, based at least in part on the determined vehicleacceleration.
 22. The mobile computing system of claim 21, wherein theprocessing unit is configured to determine the vehicle velocity furtherbased on a median of the constants for the wheel rotation measurements.23. A device comprising: means for estimating a total acceleration basedon acceleration measurements of a vehicle taken during a period of time;means for estimating a linear velocity and an acceleration based onwheel rotation measurements of the vehicle taken during the period oftime; means for estimating an angular velocity based on measurements ofrotational movement of the vehicle taken during the period of time;means for determining vehicle acceleration based on the estimated linearvelocity, estimated total acceleration, and estimated angular velocity;means for determining gravitational acceleration based on the estimatedtotal acceleration and determined vehicle acceleration; and means foroffsetting a subsequent acceleration measurement based on the determinedgravitational acceleration.
 24. The device of claim 23, furthercomprising: means for determining a vehicle velocity using the estimatedangular velocity and estimated linear velocity; and means for using thedetermined vehicle velocity to initialize a navigation filter.
 25. Thedevice of claim 23, wherein the means for estimating the linear velocityand the means for estimating the total acceleration further comprisemeans for translating the wheel rotation measurements from a firstcoordinate frame to a second coordinate frame.
 26. The device of claim23, further comprising means for obtaining the acceleration measurementsof the vehicle and measurements of rotational movement of the vehiclefrom an inertial measurement unit (IMU) located on the vehicle.
 27. Thedevice of claim 26, wherein the IMU composes a portion of a visualinertial odometry (VIO) means located on the vehicle.
 28. The device ofclaim 23, further comprising means for estimating a location of thevehicle based at least in part on the offset subsequent accelerationmeasurement.
 29. The device of claim 23, further comprising means forperforming outlier compensation on the wheel rotation measurements,measurements of rotational movement of the vehicle, or accelerationmeasurements of the vehicle, or any combination thereof.
 30. The deviceof claim 29, wherein the means for performing outlier compensation onthe wheel rotation measurements include means for determining values ofstaggered differences of the wheel rotation measurements.
 31. The deviceof claim 30, wherein the means for determining the vehicle accelerationcomprises means for taking a median of the determined values ofstaggered differences.
 32. The device of claim 31, further comprisingmeans for determining a vehicle velocity at least in part by using theestimated angular velocity and estimated linear velocity and means fordetermining a constant for each wheel rotation measurement, based atleast in part on the determined vehicle acceleration.
 33. The device ofclaim 32, wherein the means for determining the vehicle velocity furtheris configured to base the determining on a median of the constants forthe wheel rotation measurements.
 34. A non-transitory computer-readablemedium having instructions embedded thereon for initializing anaccelerometer located on a vehicle, wherein the instructions, whenexecuted by one or more processing units, cause the one or moreprocessing units to: estimate a total acceleration based on measurementsfrom the accelerometer taken during a period of time; estimate a linearvelocity and an acceleration based on wheel rotation measurements of thevehicle taken during the period of time; estimate an angular velocitybased on measurements from a gyroscope located on the vehicle and takenduring the period of time; determine vehicle acceleration based on theestimated linear velocity, estimated total acceleration, and estimatedangular velocity; determine gravitational acceleration based on theestimated total acceleration and determined vehicle acceleration; andoffset a subsequent measurement of the accelerometer based on thedetermined gravitational acceleration.
 35. The non-transitorycomputer-readable medium of claim 34, further comprising instructionsthat, when executed by the one or more processing units, further causethe one or more processing units to: determine a vehicle velocity usingthe estimated angular velocity and estimated linear velocity; and usethe determined vehicle velocity to initialize a navigation filter. 36.The non-transitory computer-readable medium of claim 34, wherein theinstructions for estimating the linear velocity and the instructions forestimating the total acceleration further comprise instructions that,when executed by the one or more processing units, further cause the oneor more processing units to translate the wheel rotation measurementsfrom a first coordinate frame to a second coordinate frame.
 37. Thenon-transitory computer-readable medium of claim 34, further comprisinginstructions that, when executed by the one or more processing units,further cause the one or more processing units to obtain themeasurements from the accelerometer and the measurements from thegyroscope from an inertial measurement unit (IMU) located on thevehicle.
 38. The non-transitory computer-readable medium of claim 34,further comprising instructions that, when executed by the one or moreprocessing units, further cause the one or more processing units toestimate, a location of the vehicle based at least in part on the offsetsubsequent measurement of the accelerometer.
 39. The non-transitorycomputer-readable medium of claim 34, further comprising instructionsthat, when executed by the one or more processing units, further causethe one or more processing units to perform outlier compensation on thewheel rotation measurements, measurements from the gyroscope, ormeasurements from the accelerometer, or any combination thereof.
 40. Thenon-transitory computer-readable medium of claim 39, wherein theinstructions for performing outlier compensation on the wheel rotationmeasurements comprise instructions for determining values of staggereddifferences of the wheel rotation measurements.
 41. The non-transitorycomputer-readable medium of claim 40, wherein the instructions fordetermining the vehicle acceleration comprise instructions for taking amedian of the determined values of staggered differences.
 42. Thenon-transitory computer-readable medium of claim 41, further comprisinginstructions that, when executed by the one or more processing units,further cause the one or more processing units to determine a vehiclevelocity at least in part by using the estimated angular velocity andestimated linear velocity and determine a constant for each wheelrotation measurement, based at least in part on the determined vehicleacceleration.
 43. The non-transitory computer-readable medium of claim42, wherein the instructions for determining the vehicle velocityfurther include instructions for basing the determining on a median ofthe constants for the wheel rotation measurements.